Role of ubiquitous sensor network in patient care

Efficient caring for the patient's information is an important aspect of caring for the patient. If these processes are possible to monitor anytime anywhere as per the patients' and doctors desecrations the cost of patient care could be minimised. In this connection, Ubiquitous Sensor Network is playing a key role on communication between physicians and patients as well as information sharing among health care providers with rapid access to medical information through reliable and trusted computer network systems. This paper argues possibilities of such scenarios by introducing a ubiquitous sensor network in patient care for 21st century's requirements and standards.

Mastering the canonical loop of serine protease inhibitors : enhancing potency by optimising the internal hydrogen bond network

Background Canonical serine protease inhibitors commonly bind to their targets through a rigid loop stabilised by an internal hydrogen bond network and disulfide bond(s). The smallest of these is sunflower trypsin inhibitor (SFTI-1), a potent and broad-range protease inhibitor. Recently, we re-engineered the contact β-sheet of SFTI-1 to produce a selective inhibitor of kallikrein-related peptidase 4 (KLK4), a protease associated with prostate cancer progression. However, modifications in the binding loop to achieve specificity may compromise structural rigidity and prevent re-engineered inhibitors from reaching optimal binding affinity. Methodology/Principal Findings In this study, the effect of amino acid substitutions on the internal hydrogen bonding network of SFTI were investigated using an in silico screen of inhibitor variants in complex with KLK4 or trypsin. Substitutions favouring internal hydrogen bond formation directly correlated with increased potency of inhibition in vitro. This produced a second generation inhibitor (SFTI-FCQR Asn14) which displayed both a 125-fold increased capacity to inhibit KLK4 (Ki = 0.0386±0.0060 nM) and enhanced selectivity over off-target serine proteases. Further, SFTI-FCQR Asn14 was stable in cell culture and bioavailable in mice when administered by intraperitoneal perfusion. Conclusion/Significance These findings highlight the importance of conserving structural rigidity of the binding loop in addition to optimising protease/inhibitor contacts when re-engineering canonical serine protease inhibitors.

Granule mining and its application for network traffic characterization

Decision table and decision rules play an important role in rough set based data analysis, which compress databases into granules and describe the associations between granules. Granule mining was also proposed to interpret decision rules in terms of association rules and multi-tier structure. In this paper, we further extend granule mining to describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other ganules, it provides a kind of novel knowledge in databases. Some experiments are conducted to test the proposed new concepts for describing the characteristics of a real network traffic data collection. The results show that the proposed concepts are promising.

Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.

Strategizing public sector human resource management : the implications of working network

Networks have come to the fore as a means by which government can achieve its strategic objectives, particularly when addressing complex or “wicked” issues. Such joined-up arrangements differ in their operations from other forms of organizing as they require collaborative effort to deliver the collaborative advantage. Strategic Human Resource Management is concerned with the matching of human resource practices to the strategic direction of organizations. It is argued that the strategic direction of government has been towards network involvement and that, as a result, a reconfiguration of Human Resource Management practices is needed to support this new direction. Drawing on eight network case studies findings are presented in relation to the roles government is expected to play in networks and conclusions are drawn about what types of human resource management practices would best support those roles. Implications for Strategic Human Resource Management are posited.

Assessing the representativeness of a groundwater quality monitoring network

The monitoring sites comprising a state of the environment (SOE) network must be carefully selected to ensure that they will be representative of the broader resource. Hierarchical cluster analysis (HCA) is a data-driven technique that can potentially be employed to assess the representativeness of a SOE monitoring network. The objective of this paper is to explore the use of HCA as an approach for assessing the representativeness of the New Zealand National Groundwater Monitoring Programme (NGMP), which is comprised of 110 monitoring sites across the country.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

Development of network-level project screening methods supporting the 4-Year pavement management plan in Texas

The Texas Department of Transportation (TxDOT) is concerned about the widening gap between pavement preservation needs and available funding. Thus, the TxDOT Austin District Pavement Engineer (DPE) has investigated methods to strategically allocate available pavement funding to potential projects that improve the overall performance of the District and Texas highway systems. The primary objective of the study presented in this paper is to develop a network-level project screening and ranking method that supports the Austin District 4-year pavement management plan development. The study developed candidate project selection and ranking algorithms that evaluated pavement conditions of each project candidate using data contained in the Pavement Management Information system (PMIS) database and incorporated insights from Austin District pavement experts; and implemented the developed method and supporting algorithm. This process previously required weeks to complete, but now requires about 10 minutes including data preparation and running the analysis algorithm, which enables the Austin DPE to devote more time and resources to conducting field visits, performing project-level evaluation and testing candidate projects. The case study results showed that the proposed method assisted the DPE in evaluating and prioritizing projects and allocating funds to the right projects at the right time.

Low cost railway level crossings

The Cooperative Research Centre (CRC) for Rail Innovation is conducting a tranche of industry-led research projects looking into safer rail level crossings. This paper will provide an overview of the Affordable Level Crossings project, a project that is performing research in both engineering and human factors aspects of low-cost level crossing warning devices (LCLCWDs), and is facilitating a comparative trial of these devices over a period of 12 months in several jurisdictions. Low-cost level crossing warning devices (LCLCWDs) are characterised by the use of alternative technologies for high cost components including train detection and connectivity (e.g. radar, acoustic, magnetic induction train detection systems and wireless connectivity replacing traditional track circuits and wiring). These devices often make use of solar power where mains power is not available, and aim to make substantial savings in lifecycle costs. The project involves trialling low-cost level crossing warning devices in shadow-mode, where devices are installed without the road-user interface at a number of existing level crossing sites that are already equipped with conventional active warning systems. It may be possible that the deployment of lower-cost devices can provide a significantly larger safety benefit over the network than a deployment of expensive conventional devices, as the lower cost would allow more passive level crossing sites to be upgraded with the same capital investment. The project will investigate reliability and safety integrity issues of the low-cost devices, as well as evaluate lifecycle costs and investigate human factors issues related to warning reliability. This paper will focus on the requirements and safety issues of LCLCWDs, and will provide an overview of the Rail CRC projects.